impl svd in torch frontend, all tests passing

ivy/functional/frontends/torch/linalg.py

@@ -52,3 +52,8 @@ def matrix_power(input, n, *, out=None):
 @to_ivy_arrays_and_back
 def matrix_rank(input, *, atol=None, rtol=None, hermitian=False, out=None):
     return ivy.astype(ivy.matrix_rank(input, atol=atol, rtol=rtol, out=out), ivy.int64)
+
+
+@to_ivy_arrays_and_back
+def svd(input, /, *, full_matrices=True):
+    return ivy.svd(input, compute_uv=True, full_matrices=full_matrices)

ivy_tests/test_ivy/test_frontends/test_torch/test_linalg.py

@@ -3,8 +3,11 @@
 import numpy as np
 from hypothesis import strategies as st
 
+
 # local
+import ivy
 import ivy_tests.test_ivy.helpers as helpers
+from ivy_tests.test_ivy.helpers import assert_all_close
 from ivy_tests.test_ivy.helpers import handle_frontend_test
 
 
@@ -277,3 +280,60 @@ def test_matrix_rank(
         rtol=rtol,
         atol=atol,
     )
+
+
+# svd
+@handle_frontend_test(
+    fn_tree="torch.linalg.svd",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        min_value=0,
+        max_value=10,
+        shape=helpers.ints(min_value=2, max_value=5).map(lambda x: tuple([x, x])),
+    ),
+    full_matrices=st.booleans(),
+)
+def test_torch_svd(
+    *,
+    dtype_and_x,
+    full_matrices,
+    with_out,
+    num_positional_args,
+    as_variable,
+    native_array,
+    frontend,
+    fn_tree,
+    on_device,
+):
+    dtype, x = dtype_and_x
+    x = np.asarray(x[0], dtype=dtype[0])
+    # make symmetric positive definite beforehand
+    x = np.matmul(x.T, x) + np.identity(x.shape[0]) * 1e-3
+    ret, frontend_ret = helpers.test_frontend_function(
+        input_dtypes=dtype,
+        as_variable_flags=as_variable,
+        with_out=with_out,
+        num_positional_args=num_positional_args,
+        native_array_flags=native_array,
+        frontend=frontend,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        test_values=False,
+        atol=1e-03,
+        rtol=1e-05,
+        input=x,
+        full_matrices=full_matrices,
+    )
+    ret = [ivy.to_numpy(x) for x in ret]
+    frontend_ret = [np.asarray(x) for x in frontend_ret]
+
+    u, s, vh = ret
+    frontend_u, frontend_s, frontend_vh = frontend_ret
+
+    assert_all_close(
+        ret_np=u @ np.diag(s) @ vh,
+        ret_from_gt_np=frontend_u @ np.diag(frontend_s) @ frontend_vh,
+        rtol=1e-2,
+        atol=1e-2,
+        ground_truth_backend=frontend,
+    )